Sebastian Schönherr

Unique Molecular Identifiers (UMIs) enable efficient error correction in amplicon sequencing but UMI-aware analysis workflows for long-read sequencing and particularly for nanopore data are still sparse. Existing approaches lack portability, real-time sequencing support, GPU acceleration and efficient use of resources. We present umi-pipeline-nf, a portable, fully containerized, modular and scalable workflow to create single-molecule consensus sequences from UMI-tagged long-read nanopore amplicon data. Umi-pipeline-nf supports flexible UMI-designs and is built in Nextflow DSL2 for seamless deployment across computing platforms and a high degree of parallelization, allowing analysis of several targets at once. It scales linearly from single samples to large cohorts, outperforming existing tools in efficiency and flexibility. Additionally, we integrated real-time read processing, robust UMI clustering and GPU-accelerated consensus polishing. Umi-pipeline-nf supports two different polishing strategies (reference sequence-based and partial order alignment [POA]-based). Implementation of GPU-accelerated, reference sequence-based polishing results in up to 100-fold runtime improvements and reduced usage of computational resources, compared to other UMI analysis pipelines and POA-based polishing. The umi-pipeline-nf analysis pipeline and test data are available at https://github.com/genepi/umi-pipeline-nf and a frozen snapshot is available at DOI: 10.5281/zenodo.18607956. Scripts and configuration files for the analyses in the present manuscript can be found at https://github.com/AmstlerStephan/umi-pipeline-nf_Paper. Supplementary data are available at Bioinformatics online.

CRISPR-dependent base editing (BE) enables the modeling and correction of genetic mutations at single-base resolution. Base editing screens, where point mutations are queried en masse, are powerful tools to systematically draw genotype–phenotype associations and characterise the function of genes and other genomic elements. However, the lack of user-friendly web-based tools for designing base editing screens can hinder broad technology adoption. Here, we introduce CRISPR-BEasy (https://crispr-beasy.cerc-genomic-medicine.ca), a free, automated web-based server that streamlines the creation of single guide (sg)RNA tiling libraries for base editing screens. Researchers can provide their genes or genomic features of interest, their base editors of choice, and target sequences to act as positive and negative controls. The server designs and annotates sgRNA libraries by integrating custom code with publicly available tools such as crisprVerse and Ensembl’s Variant Effect Predictor. CRISPR-BEasy provides downloadable results, including sgRNA on/off-target scores, predicted mutational outcomes per base editor, and intuitive interactive visualizations for data quality assessment. CRISPR-BEasy also provides a separate tool that assembles sgRNA libraries into oligonucleotides for cloning following the detailed protocol documented in the searchable web server manual. Together, CRISPR-BEasy ensures the seamless design of cloning-ready sgRNA libraries, seeking to democratise access to base editing screening technologies.

Background: The workflow management system Nextflow, together with the nf-core community, has established an essential ecosystem in bioinformatics. However, ensuring the correctness and reliability of large and complex Nextflow pipelines remains challenging due to the lack of a unified, automated unit-testing framework. Results: To address this gap, we present nf-test, a modular testing framework for bioinformatics workflows. It enables users to test process blocks, workflow patterns, and entire pipelines in isolation while validating their outputs. Built with a syntax similar to Nextflow DSL2, nf-test offers unique features such as snapshot testing and smart testing, which optimize resource usage by testing only modified modules. We demonstrate across multiple pipelines that these features minimize development time, reduce test execution time by up to 80%, and enhance software quality by identifying bugs and issues early in the development process. Conclusions: Already adopted by numerous pipelines, nf-test significantly improves the robustness, maintainability, and reliability of bioinformatics pipelines.

Summary: Nextflow has emerged as a flexible and scalable workflow system for building computational pipelines in life sciences. Simultaneously, Software-as-a-Service platforms enable researchers to concentrate on data analysis rather than on setting up pipelines locally. However, converting existing pipelines into web services can be challenging due to complexities such as handling large datasets, web development and overall security. Cloudgene 3 addresses these challenges by providing a platform that transforms Nextflow pipelines into scalable web services in just a few steps. Making pipelines available as public or private services enhances collaboration among diverse teams and improves accessibility, eliminating the need for extensive technical expertise. Availability and Implementation: Cloudgene 3 is free available at https://www.cloudgene.io Contact: lukas.forer@i-med.ac.at

Background: Variable number tandem repeats (VNTRs) are highly polymorphic DNA regions harboring many potentially disease-causing variants. However, VNTRs often appear unresolved (“dark”) in variation databases due to their repetitive nature. One particularly complex and medically relevant VNTR is the KIV-2 VNTR located in the cardiovascular disease gene LPA which encompasses up to 70% of the coding sequence. Results: Using the highly complex LPA gene as a model, we develop a computational approach to resolve intra-repeat variation in VNTRs from largely available short-read sequencing data. We apply the approach to six protein-coding VNTRs in 2504 samples from the 1000 Genomes Project and developed an optimized method for the LPA KIV-2 VNTR that discriminates the confounding KIV-2 subtypes upfront. This results in an F1-score improvement of up to 2.1-fold compared to previously published strategies. Finally, we analyze the LPA VNTR in > 199,000 UK Biobank samples, detecting > 700 KIV-2 mutations. This approach successfully reveals new strong Lp(a)-lowering effects for KIV-2 variants, with protective effect against coronary artery disease, and also validated previous findings based on tagging SNPs. Conclusions: Our approach paves the way for reliable variant detection in VNTRs at scale and we show that it is transferable to other dark regions, which will help unlock medical information hidden in VNTRs.

Lipoprotein (a) (Lp(a)) is a risk factor for cardiovascular diseases and mainly regulated by the complex LPA gene. We investigated the types of variation in the LPA gene and their predictive performance on Lp(a) concentration. We determined the Kringle IV-type 2 (KIV-2) copy number (CN) using the DRAGEN LPA Caller (DLC) and a read depth-based CN estimator in 8351 short-read whole genome sequencing samples from the GENESIS-HD study. The pentanucleotide repeat in the promoter region was genotyped with GangSTR and ExpansionHunter. Lp(a) concentration was available in 4861 population-based subjects. Predictive performance on Lp(a) concentration was investigated using random forests. The agreement of the KIV-2 CN between the two specialized callers was high (r = 0.9966; 95% confidence interval [CI] 0.9965–0.9968). Allele-specific KIV-2 CN could be determined in 47.0% of the subjects using the DLC. Lp(a) concentration can be better predicted from allele-specific KIV-2 CN than total KIV-2 CN. Two single nucleotide variants, 4925G>A and rs41272114C>T, further improved prediction. The genetically complex LPA gene can be analyzed with excellent agreement between different callers. The allele-specific KIV-2 CN is more important for predicting Lp(a) concentration than the total KIV-2 CN.

The workflow management system Nextflow builds together with the nf-core community an essential ecosystem in Bioinformatics. However, ensuring the correctness and reliability of large and complex pipelines is challenging, since a unified and automated unit-style testing framework specific to Nextflow is still missing. To provide this crucial component to the community, we developed the testing framework nf-test. It introduces a modular approach that enables pipeline developers to test individual process blocks, workflow patterns and entire pipelines in insolation. nf-test is based on a similar syntax as Nextflow DSL 2 and provides unique features such as snapshot testing and smart testing to save resources by testing only changed modules. We show on different pipelines that these improvements minimize development time, reduce test execution time by up to 80% and enhance software quality by identifying bugs and issues early. Already adopted by dozens of pipelines, nf-test improves the robustness and reliability in pipeline development.

Over the past decade, mtDNA-Server established itself as one of the most widely used variant calling web-services for human mitochondrial genomes. The service accepts sequencing data in BAM format and returns an annotated variant analysis report for both homoplasmic and heteroplasmic variants. In this work we present mtDNA-Server 2, which includes several new features highly requested by the community. Most importantly, it includes (a) the integration of a novel variant calling mode that accurately call insertions, deletions and single nucleotide variants at once, (b) the integration of additional quality control and input validation modules, (c) a method to estimate the required coverage to minimize false positives and (d) an interactive analytics dashboard. Furthermore, we migrated the complete analysis workflow to the Nextflow workflow manager for improved parallelization, reproducibility and local execution. Recognizing the importance of insertions and deletions as well as offering novel quality control, validation and reporting features, mtDNA-Server 2 provides researchers and clinicians a new state-of-the-art analysis platform for interpreting mitochondrial genomes. mtDNA-Server 2 is available via mitoverse, our analysis platform that offers a centralized place for mtDNA analysis in the cloud. The web-service, source code and its documentation are freely accessible at https://mitoverse.i-med.ac.at.